Subtype-specific associations between breast cancer risk polymorphisms and the survival of early-stage breast cancer

This is a hospital-based study including 1177 early breast cancer cases from Fujian Medical University Union Hospital from July 2000 and October 2014. All the participants were histopathologically confirmed with invasive breast cancer and subsequently treated with curative surgical resection and systemic therapy. Clinicopathological and demographic data were collected from the hospital records and survival data were obtained from the followed-up database which was renewed annually. The patients were staged according to the 7th version of American Joint Commission on Cancer (AJCC) tumor-node-metastasis (TNM) staging system [10]. Estrogen receptor (ER)/progesterone receptor (PR) positivity was determined by IHC analysis of the number of positively stained nuclei (≥ 10%) and hormone receptor (HR) positivity was defined as being either ER+ and/or PR+. Tumors were considered human epidermal growth factor-2 (HER2) positive when cells exhibited strong membrane staining (3+). Expressions of 2+ would require further in situ hybridization testing for HER2 gene amplification while expressions of 0 or 1+ were regarded as negative. The subtypes were categorized as follows [11]: luminal A (ER+, PR+ > 20%, HER2−, Ki67 < 14% or grade I when Ki67 was unavailable), luminal B (HR+, HER2−, Ki67 > 14% or grade II/III when Ki67 was unavailable or HR+, HER2+); HER2 enriched (HR−, HER2+) and triple negative (HR− and HER2−). The study was approved by the Institutional Ethics Committee and all participants consented to genetic testing at the time of their participation and contributed data.

We selected the polymorphisms associated with breast cancer susceptibility from the US National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies. We used the following inclusion criteria: (i) the significance level for genome-wide association was considered to be P ≤ 1 × 10⁻⁹; (ii) the minor allele frequency (MAF) was at least 10% in the HapMap CHB data of the public SNP database (http://​www.​ncbi.​nlm.​nih.​gov/​SNP); (iii) pair wise linkage disequilibrium (LD) between the eligible SNPs calculated by Haploview 4.1 software must be less than 0.8 (r² < 0.8). At last, 21 polymorphisms were applied in this study which can be found in Additional file 1: Table S1.

Blood samples were collected in EDTA anticoagulant tubes and stored at − 80 °C until DNA extraction. Genomic DNA was extracted using the Whole-Blood DNA Extraction Kit (Bioteke, Beijing, China), according to the manufacturer’s protocol. The genotype analysis was performed by SNPscan, which is a high-throughput SNPs genotyping technology (Genesky Biotechnologies Inc., Shanghai, China). Finally, the raw data were analyzed by the GeneMapper 4.0 Software (Applied Biosystems, Foster City, CA). 5% of samples were randomly selected as blinded duplicates for quality assessment purposes and 100% concordance was obtained.

Overall survival (OS) and breast cancer specific survival (BCSS) were our primary endpoints and defined as the time from the date of cancer diagnosis to the date of mortality for all cause and breast cancer, respectively. Disease free survival (DFS) and distant disease free survival (DDFS) were our secondary endpoints and calculated separately as the time from the date of diagnosis to the date of any recurrence and distant recurrence to the last patient contact [12]. Survival data were analyzed using the Kaplan–Meier method with the log-rank test and multivariate Cox stepwise regression analysis to the end of follow-up (2016.12.31). Adjustment for age at diagnosis, tumor size, lymph node involvement, histological grade, ER status, and HER-2/neu expression were applied. The hazard ratios (HRs) and 95% confidence interval (CI) for each factor in multivariate analyses were calculated from the Cox-regression model. The Chi square-based Q test was used to examine the heterogeneity between subgroups. The possible gene-environment interactions were also evaluated by the Cox proportional hazard regression models. All tests were 2-sided, and P values of < 0.05 were considered statistically significant. SAS 9.4 (SAS Institute Inc., Cary, NC) was used for all statistical analyses.

Patients’ clinical characteristics and survival are summarized in Table 1. All the 1177 early breast cancer cohort, were female and their mean age was 47.0 ± 10.3 years old at breast cancer diagnosis. During a median follow-up time of 174 months, 446 cases experienced recurrence (142 locoregional and 410 distant) and 343 died (333 died of BC and 10 died of other disease).

No significant difference in BC-DDFS, BCSS, and OS was shown in the subgroup of age at diagnosis (P = 0.087, 0.420, and 0.402). But patients with a tumor size > 2 cm, lymph node positive, grade III, clinical stage II + III, or HER2 positive had significantly shorter survival times, whereas being ER or HR positivity remarkably improved the survival of EBC patients (log-rank P < 0.05, Table 1). Furthermore, our intrinsic molecular subtypes (luminal A, luminal B, HER2-enriched, and triple negative) were also associated with significantly different survival (log-rank P < 0.05, Table 1).

Among the 21 SNPs, 6 SNPs (rs13281615, rs4415084, rs4784227, rs889312, rs10474352 and rs10816625) had a log-rank P under 0.05 in some genetic models and in some outcome indicators (log-rank P < 0.05, Table 2). But after adjusting for age at breast cancer diagnosis, tumor size, lymph node involvement, grade, hormone receptor status, and HER2 status, only rs889312 and rs2046210 had significant effect on improving survival of EBC patients. In a recessive model, rs889312 was significantly associated with better iDFS and DDFS (iDFS: adjusted HR (aHR): 0.761, 95% CI 0.583–0.994, and DDFS: aHR: 0.631, 95% CI 0.470–0.848; Table 3). Similarly, in contrast to the GG + AA genotypes, the GA genotype of rs2046210 also improve the survival of EBC patients (iDFS: aHR: 0.812, 95% CI 0.673–0.980; DDFS: aHR: 0.771, 95% CI 0.635–0.938; BCSS: aHR: 0.790, 95% CI 0.636–0.981 and OS aHR: 0.786, 95% CI 0.635–0.934, Table 3).

For a large number of patients enrolled in this study, we analyzed the association between enrolled SNPs and survival associated with different molecular subtypes of EBC. As showed in Table 3, rs9485372 and rs4415084 were still associated with a worse outcome in luminal A and triple negative EBC patients, respectively, after adjustment (for rs9485372 under the recessive model: iDFS: aHR: 2.465, 95% CI 1.133–5.360; DDFS: aHR: 2.671, 95% CI 1.214–5.875; BCSS and OS: aHR: 3.522, 95% CI 1.464–8.473; for rs4415084 under the dominant model: iDFS: aHR: 1.674, 95% CI 1.043–2.687; DDFS: aHR: 1.804, 95% CI 1.084–3.002 and OS: aHR: 1.674, 95% CI 1.000–2.803). Furthermore, in the luminal B subtype we found that rs4951011 (under the dominant model) and rs889312 (under the recessive model) could significantly improve the iDFS, DDFS, BCSS and OS of the breast cancer, while rs9485372 (under dominant model) worsens outcome (iDFS: aHR = 0.719, 95% CI 0.557–0.928, DDFS: aHR = 0.734, 95% CI 0.561–0.960, BCSS: aHR = 0.721, 95% CI 0.528–0.984 and OS: aHR = 0.690, 95% CI 0.510–0.934 for rs4951011; iDFS: aHR = 0.558, 95% CI 0.381–0.817, DDFS: aHR = 0.419, 95% CI 0.269–0.653, BCSS: aHR = 0.498, 95% CI 0.304–0.815 and OS: aHR = 0.465, 95% CI 0.285–0.761 for rs889312 and iDFS: aHR = 1.482, 95% CI 0.124–1.954, DDFS: aHR = 1.557, 95% CI 0.161–2.088, BCSS: aHR = 1.504, 95% CI 1.071–2.112 and OS: aHR = 1.538, 95% CI 1.104–2.142 for 9485872, Table 3). However, no significant effect was observed in the HER2-enriched subtype in any model of the 21 polymorphisms.

To assess the combined effects on risk of recurrence and death from luminal B EBC, we combined the risk genotypes of rs4951011, rs889312 and 9485372. According to the number of combined risk genotypes, the univariate survival analysis show that all of iDFS, DDFS, BCSS and OS were significantly different among different groups with different combined risk genotypes (P Log-rank < 0.01) (Fig. 1). As shown in Table 4, compared to subjects with one or no unfavorable genotype, subjects carrying more unfavorable loci had shorter survival time and had a 1.534–1.645 fold increased risk of recurrence and/of death even after adjustment (iDFS: aHR = 1.534, 95% CI 1.288–1.827, DDFS: aHR = 1.632, 95% CI 1.356–1.964, BCSS: aHR = 1.570, 95% CI 1.267–1.944 and OS: aHR = 1.645, 95% CI 1.334–2.029, respectively for trend).

The associations between breast cancer risk loci genotypes and EBC survival were then evaluated by stratified analysis of age at diagnosis, tumor size, lymph node involvement, grade, hormone-receptor status and HER2 status. As shown in Table 5, we found that rs4415084 and rs2981582 were associated with shorter survival of the patients who were younger (rs4415084 for age at diagnosis ≤ 35 years: iDFS: aHR = 1.792, 95% CI 1.161–2.915, DDFS: aHR = 2.172, 95% CI 1.310–3.602, BCSS: aHR = 2.250, 95% CI 1.278–3.959 and OS: aHR = 1.871, 95% CI 0.988–3.544) and with higher grade tumors (rs2981582 for grade III: iDFS: aHR = 1.666, 95% CI 1.051–2.639, DDFS: aHR = 1.682, 95% CI 1.049–2.698, BCSS: aHR = 1.783, 95% CI 1.080–2.944 and OS: aHR = 1.732, 95% CI 1.050–2.855). But rs2046210 and rs3803662 had beneficial effects on survival of the patients with larger tumor (rs2046210 for tumor size > 2 cm: iDFS: aHR = 0.757, 95% CI 0.606–0.944, DDFS: aHR = 0.732, 95% CI 0.582–0.919, BCSS: aHR = 0.713, 95% CI 0.533–0.920 and OS: aHR = 0.694, 95% CI 0.540–0.992) and with higher grade tumors (rs3803662 for grade III: iDFS: aHR = 0.588, 95% CI 0.414–0.834, DDFS: aHR = 0.586, 95% CI 0.407–0.845, BCSS: aHR = 0.479, 95% CI 0.319–0.717 and OS: aHR = 0.484, 95% CI 0.324–0.722) respectively. However, we did not find that the other SNPs affected survival in the subgroups of patients with different tumor characteristics.

An interaction analysis was performed (Table 6) and statistically significant multiplicative interactions on EBC survival were found both between rs4415084 genotypes and age at diagnosis (adjusted Pint: iDFS 0.045, DDFS 0.013, BCSS 0.025 and OS 0.018) and between rs3803662 genotypes and tumor grade (adjusted Pint: iDFS 0.011, DDFS 0.001, BCSS 4.7 × 10⁻⁴ and OS 9.9 × 10⁻⁴).